ABSTRACT :
Link prediction is an important issue in graph data mining. In social networks, link prediction is used to predict missing links in current networks and new links in future networks. This process has a wide range of applications including recommender systems, spam mail classification, and the identification of domain experts in various research areas. In order to predict future node similarity, we propose a new model, Common Influence Set, to calculate node similarities. The proposed link prediction algorithm uses the common influence set of two unconnected nodes to calculate a similarity score between the two nodes. We used the area under the ROC curve (AUC) to evaluate the performance of our algorithm and that of previous link prediction algorithms based on similarity over a range of problems. Our experimental results show that our algorithm outperforms previous algorithms.
EXISTING SYSTEM :
In a social network G(V, E, W), where V is the set of nodes, and E is the set of edges, We(u, v) represents the probability of propagation of node u to node v. Link prediction aims to identify unobserved or missing edges in the current network G. In this paper we propose a novel algorithm to identify unobserved or missing edges in a network, in descending order the similarity of each pair of such nodes, such that the higher the similarity of two nodes, the greater the possibility is of a link existing.
Because the complexity of this computation is high, the calculation takes a long time. We therefore use a proximate model to calculate the similarity between nodes. In this paper, we use the MIA model to calculate influence. The MIA model uses maximum influence path propagation probability as the influence value between two nodes.
DISADVANTAGES OF EXISTING SYSTEM :
1) Less accuracy
2)low Efficiency
PROPOSED SYSTEM :
Links prediction involves two primary methods: namely, structural and feature-based. Most of the structural-based link prediction methods use network structure to measure node similarities. For example, in a social network, two individuals with many common friends are more likely connect in future [16]. Lada and Adar [1] proposed a method based on common neighbors to predict relationships between individuals. Murata and Moriyasu [15] proposed a link prediction method which constructed a directed action graph to estimate the similarity of the existence of a link between two nodes in weighted networks. Liu et al. [14] proposed a similarity score based on a common neighbor method mentioned before and LBN(local naive Bayes) which performs better than common neighbors. Paths between nodes may also be used for link prediction, Katz [10] used the number of paths between two nodes and their length, producing reasonable results. Lü et al. [11] proposed approach which had high effectiveness and efficiency, a local path index, to estimate the probability of the existence of a link between two nodes. Liu and Lü [13] proposed a method that use a local random walk to estimate the probability of the existence of a link between two nodes. Wang et al. [24] proposed a method that uses a clusteringbased collaborative filtering approach, including both topological and node attributes. Xu et al. [25] proposed a method that use path entropy as similarity index to measure nodes’ similarity. Shang et al. [22] first proposed a method for using past links to predict the future links. In [21], Shang et al. found that if a pair of nodes are connected, they are more likely to connect to the common nodes in the future networks, and they first use the past links and future links for link prediction. In [20], Shang et al. proposed the metric Precision for the evolving networks. Lee and Tukhvatov [27] proposed a topology-based similarity measure to predict future friends
ADVANTAGES OF PROPOSED SYSTEM :
1) High accuracy
2)High efficiency
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
